In [1]:
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
import glob
In [2]:
# CAMERA CALIBRATION
# find corners
def find_corners(img_list):
objpoints = [] # 3D
imgpoints = [] # 2D
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2) # create blank grid
for image in img_list: # for all images, create large array of 3D and 2D points
img = cv2.imread(image)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(gray, (9, 6), None)
if ret == True:
imgpoints.append(corners)
objpoints.append(objp)
return imgpoints, objpoints
# calibrate camera
def calibrate_cam(img, imgpoints, objpoints):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
dst = cv2.undistort(img, mtx, dist, None, mtx)
return dst
image_folder = glob.glob("camera_cal/*")
imgpoints, objpoints = find_corners(image_folder)
In [3]:
# TESTING CAMERA CALIBRATION
test_img_chessboard = mpimg.imread(image_folder[4])
dst = calibrate_cam(test_img_chessboard, imgpoints, objpoints)
# display
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(test_img_chessboard)
ax1.set_title('original image', fontsize=40)
ax2.imshow(dst)
ax2.set_title('calibrated image', fontsize=40)
plt.show()
In [4]:
# CALIBRATOIN APPLIED TO ROAD IMAGE
road_image_folder = glob.glob("test_images/*")
orig_image = mpimg.imread(road_image_folder[3])
calibration_test_img = np.copy(orig_image)
calibrated_image = calibrate_cam(calibration_test_img, imgpoints, objpoints)
# display
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(calibration_test_img)
ax1.set_title('original image', fontsize=40)
ax2.imshow(calibrated_image)
ax2.set_title('calibrated image', fontsize=40)
plt.show()
In [5]:
# THRESHOLDED BINARY IMAGE (color transforms, gradients, etc.)
def thresh_binary(img, s_thresh=(135, 255), sx_thresh=(45, 140)):
img = np.copy(img)
# Convert to HLS color space and separate the V channel
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
l_channel = hls[:,:,1]
s_channel = hls[:,:,2]
# Sobel x
sobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0) # Take the derivative in x
abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
# Threshold x gradient
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1
# Threshold color channel
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1
# Stack each channel
color_transform = np.dstack(( np.zeros_like(sxbinary), sxbinary, s_binary)) * 255
binary_transform = np.zeros_like(sxbinary)
binary_transform[(s_binary == 1) | (sxbinary == 1)] = 1
return color_transform, binary_transform
color_transform, binary_transform = thresh_binary(calibrated_image)
# display
f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(24, 9))
f.tight_layout()
ax1.imshow(calibrated_image)
ax1.set_title('calibrated image', fontsize=40)
ax2.imshow(color_transform)
ax2.set_title('color transform', fontsize=40)
ax3.imshow(binary_transform, cmap='gray')
ax3.set_title('binary transform', fontsize=40)
plt.show()
In [6]:
# PERSPECTIVE TRANSFORM
# need to run the perspective transform, display it, then have the same function for each picture (quick)
def perspective_transform_matrix(img):
img_size = (img.shape[1], img.shape[0])
corners = np.float32(
[[(img_size[0] / 2) - 54, img_size[1] / 2 + 95],
[((img_size[0] / 6) - 15), img_size[1]],
[(img_size[0] * 5 / 6) + 60, img_size[1]],
[(img_size[0] / 2 + 60), img_size[1] / 2 + 95]])
warped_corners = np.float32(
[[(img_size[0] / 4), 0],
[(img_size[0] / 4), img_size[1]],
[(img_size[0] * 3 / 4), img_size[1]],
[(img_size[0] * 3 / 4), 0]])
M = cv2.getPerspectiveTransform(corners, warped_corners)
Minv = cv2.getPerspectiveTransform(warped_corners, corners)
birds_eye = cv2.warpPerspective(img, M, img_size)
birds_eye_with_box = cv2.polylines(birds_eye, np.int32([warped_corners]), True, (0, 255, 0), 12)
superimposed_img = cv2.polylines(img, np.int32([corners]), True, (0, 255, 0), 8)
return birds_eye, birds_eye_with_box, superimposed_img, M, Minv
perspective_transform_input = np.copy(calibrated_image)
birds_eye, birds_eye_with_box, superimposed_img, M, Minv = perspective_transform_matrix(perspective_transform_input)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(superimposed_img)
ax1.set_title('superimposed image', fontsize=40)
ax2.imshow(birds_eye_with_box)
ax2.set_title('bird\'s eye', fontsize=40)
plt.show()
In [7]:
# FIND LANE BOUNDARY
# HISTOGRAM PEAKS
lane_finder_input, birds_eye_with_box, superimposed_img, M, Minv = perspective_transform_matrix(binary_transform)
def hist(img):
bottom_half = img[img.shape[0]//2:,:]
histogram = np.sum(bottom_half, axis=0)
return histogram
histogram = hist(lane_finder_input)
# from lesson:
def find_lane_pixels(lane_finder_input):
# Take a histogram of the bottom half of the image
histogram = np.sum(lane_finder_input[lane_finder_input.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((lane_finder_input, lane_finder_input, lane_finder_input))
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]//2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# HYPERPARAMETERS
# Choose the number of sliding windows
nwindows = 12
# Set the width of the windows +/- margin
margin = 50
# Set minimum number of pixels found to recenter window
minpix = 60
# Set height of windows - based on nwindows above and image shape
window_height = np.int(lane_finder_input.shape[0]//nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = lane_finder_input.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated later for each window in nwindows
leftx_current = leftx_base
rightx_current = rightx_base
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = lane_finder_input.shape[0] - (window+1)*window_height
win_y_high = lane_finder_input.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),
(win_xleft_high,win_y_high),(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),
(win_xright_high,win_y_high),(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window #
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices (previously was a list of lists of pixels)
try:
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
except ValueError:
# Avoids an error if the above is not implemented fully
pass
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
return leftx, lefty, rightx, righty, out_img
def fit_polynomial(lane_finder_input):
# Find our lane pixels first
leftx, lefty, rightx, righty, out_img = find_lane_pixels(lane_finder_input)
# Fit a second order polynomial to each using `np.polyfit`
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, lane_finder_input.shape[0]-1, lane_finder_input.shape[0] )
try:
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
except TypeError:
# Avoids an error if `left` and `right_fit` are still none or incorrect
print('The function failed to fit a line!')
left_fitx = 1*ploty**2 + 1*ploty
right_fitx = 1*ploty**2 + 1*ploty
## Visualization ##
# Colors in the left and right lane regions
out_img[lefty, leftx] = [255, 0, 0]
out_img[righty, rightx] = [0, 0, 255]
# Plots the left and right polynomials on the lane lines
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
return out_img, left_fit, right_fit, ploty
out_img, left_fit, right_fit, ploty = fit_polynomial(lane_finder_input)
plt.imshow(out_img)
plt.show()
In [8]:
# FIND LANE BOUNDARY (part 2)
# left_fit = np.array([5.88716929e-06, -4.25458630e-02, 3.62626097e+02])
# right_fit = np.array([8.34396471e-06, 3.39224425e-02, 9.15527958e+02])
def fit_poly(img_shape, leftx, lefty, rightx, righty):
### TO-DO: Fit a second order polynomial to each with np.polyfit() ###
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, img_shape[0]-1, img_shape[0])
### TO-DO: Calc both polynomials using ploty, left_fit and right_fit ###
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
return left_fitx, right_fitx, ploty
def search_around_poly(lane_finder_input, left_fit, right_fit):
# HYPERPARAMETER
# Choose the width of the margin around the previous polynomial to search
# The quiz grader expects 100 here, but feel free to tune on your own!
margin = 50
# Grab activated pixels
nonzero = lane_finder_input.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
### TO-DO: Set the area of search based on activated x-values ###
### within the +/- margin of our polynomial function ###
### Hint: consider the window areas for the similarly named variables ###
### in the previous quiz, but change the windows to our new search area ###
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy +
left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) +
left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy +
right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) +
right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit new polynomials
left_fitx, right_fitx, ploty = fit_poly(lane_finder_input.shape, leftx, lefty, rightx, righty)
## Visualization ##
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((lane_finder_input, lane_finder_input, lane_finder_input))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin,
ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin,
ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
# Plot the polynomial lines onto the image
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
## End visualization steps ##
return left_fitx, right_fitx, result
# Run image through the pipeline
# Note that in your project, you'll also want to feed in the previous fits
left_fitx, right_fitx, result = search_around_poly(lane_finder_input, left_fit, right_fit)
# View your output
plt.imshow(result)
plt.show()
In [9]:
# DETERMINE CURVATURE OF LANE AND VEHICLE POSITION
out_img, left_fit, right_fit, ploty = fit_polynomial(lane_finder_input)
# left_fitx, right_fitx, ploty = search_around_poly(lane_finder_input.shape, left_fit, right_fit)
def measure_curvature_real(ploty, left_fitx, right_fitx):
# Define y-value where we want radius of curvature
# We'll choose the maximum y-value, corresponding to the bottom of the image
y_eval = np.max(ploty)
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
left_fit_curve = np.polyfit(ploty*ym_per_pix, left_fitx*xm_per_pix, 2)
right_fit_curve = np.polyfit(ploty*ym_per_pix, right_fitx*xm_per_pix, 2)
# Calculation of R_curve (radius of curvature)
left_curverad = ((1 + (2*left_fit[0]*y_eval*ym_per_pix + left_fit_curve[1])**2)**1.5) / np.absolute(2*left_fit_curve[0])
right_curverad = ((1 + (2*right_fit[0]*y_eval*ym_per_pix + right_fit_curve[1])**2)**1.5) / np.absolute(2*right_fit_curve[0])
return left_curverad, right_curverad
# Calculate the radius of curvature in pixels for both lane lines
left_curverad, right_curverad = measure_curvature_real(ploty, left_fitx, right_fitx)
print('left curve radius: ', left_curverad, 'm')
print('right curve radius: ', right_curverad, 'm')
In [10]:
# WARP DETECTED LANE BOUNDARIES BACK ONTO ORIGINAL IMAGE
road_image_folder = glob.glob("test_images/*")
orig_image = mpimg.imread(road_image_folder[4])
calibrated_road_image = calibrate_cam(orig_image, imgpoints, objpoints)
color_transform, binary_transform2 = thresh_binary(calibrated_road_image)
perspective_transform_input = np.copy(binary_transform2)
lane_finder_input, birds_eye_with_box, superimposed_img, M, Minv = perspective_transform_matrix(perspective_transform_input)
out_img, left_fit, right_fit, ploty = fit_polynomial(lane_finder_input)
left_fitx, right_fitx, result2 = search_around_poly(lane_finder_input, left_fit, right_fit)
def redraw(orig_image, warped, left_fitx, right_fitx, ploty, Minv):
# Create an image to draw the lines on
warp_zero = np.zeros_like(warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0, 255, 0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (orig_image.shape[1], orig_image.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(orig_image, 1, newwarp, 0.3, 0)
return result
redrawn_image = redraw(orig_image, binary_transform2, left_fitx, right_fitx, ploty, Minv)
plt.show()
plt.imshow(redrawn_image)
plt.show()
In [11]:
# PIPELINE
road_image_folder = glob.glob("test_images/*")
img = mpimg.imread(road_image_folder[3])
def pipeline(input_image):
calibrated_road_image = calibrate_cam(input_image, imgpoints, objpoints)
color_transform, binary_transform2 = thresh_binary(calibrated_road_image)
perspective_transform_input = np.copy(binary_transform2)
lane_finder_input, birds_eye_with_box, superimposed_img, M, Minv = perspective_transform_matrix(perspective_transform_input)
out_img, left_fit, right_fit, ploty = fit_polynomial(lane_finder_input)
left_fitx, right_fitx, result2 = search_around_poly(lane_finder_input, left_fit, right_fit)
left_curverad, right_curverad = measure_curvature_real(ploty, left_fitx, right_fitx)
output_image = redraw(input_image, binary_transform2, left_fitx, right_fitx, ploty, Minv)
center_lane = input_image.shape[1] / 2 - np.median(left_fitx + right_fitx) / 2
rad_delta = (left_curverad - right_curverad) / 2
if (left_curverad > (right_curverad + 200)):
cv2.putText(output_image, "Radius: R %s" % float('%.6g' % (rad_delta + right_curverad)), (100, 100), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 2)
else:
cv2.putText(output_image, "Radius: L %s" % float('%.6g' % (rad_delta + left_curverad)), (100, 100), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 2)
if (center_lane < 0):
center_lane = np.abs(center_lane) * 3.7 / 700 # meters per pixel in x dimension
cv2.putText(output_image, "Vehicle Position: %s m right of center" % float('%.3g' % (center_lane)), (100, 150), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 2)
else:
center_lane = center_lane * 3.7 / 700
cv2.putText(output_image, "Vehicle Position: %s m right of center" % float('%.3g' % (center_lane)), (100, 150), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 2)
return output_image
pipeline_result = pipeline(img)
plt.show()
plt.imshow(pipeline_result)
plt.show()
In [12]:
# CREATE VIDEO AND SAVE
from moviepy.editor import VideoFileClip
white_output = 'output_images/project_video_processed.mp4'
clip1 = VideoFileClip("project_video.mp4")
white_clip = clip1.fl_image(pipeline) #NOTE: this function expects color images!!
%time white_clip.write_videofile(white_output, audio=False)
In [ ]: